Simulation and identification of non-Poissonian noise triggers in the IceCube neutrino detector

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dc.contributor Toale, Patrick A.
dc.contributor Kung, Patrick
dc.contributor Henderson, Conor
dc.contributor.advisor Williams, Dawn R.
dc.contributor.author Larson, Michael James
dc.date.accessioned 2017-03-01T16:55:52Z
dc.date.available 2017-03-01T16:55:52Z
dc.date.issued 2013
dc.identifier.other u0015_0000001_0001464
dc.identifier.other Larson_alatus_0004M_11756
dc.identifier.uri https://ir.ua.edu/handle/123456789/1927
dc.description Electronic Thesis or Dissertation
dc.description.abstract The IceCube neutrino detector, located in the clear glacial ice at the South Pole, completed construction in 2011. The low-energy infill extension, DeepCore, forms a denser sub-detector using higher quantum efficiency photosensors. DeepCore has been taking data since May 2010 and lowers IceCube's energy threshold to about 10 GeV. These low-energy events are dim compared to higher energy events, necessitating the study of low-light backgrounds. While Monte Carlo predictions give an expected rate of approximately 6 Hz due to atmospheric muons, DeepCore records a significantly higher rate of 13.5 Hz with most of the discrepancy due to unsimulated noise events. Much of the rate difference may be resolved by rejecting especially dim events by counting the number of locally coincident hits, retaining 55% of the ν_e and 62% of the ν_μ events sampled with energies of 10-300 GeV while rejecting 96% of noise events. However, differences in the timing distributions of noise hits indicates a need for further study. A new source of correlated noise has been discovered, necessitating an updated noise simulation model. IceCube's new noise generator is able to reproduce the correlated noise in both IceCube and DeepCore sensors. A Metropolis-Hastings algorithm has been used to identify relevant parameters for nearly all of the 5160 sensors that make up the IceCube detector. Initial low quality fits reduce the rate discrepancy between data and simulation from 32% using a Poissonian noise model to 20% using the updated noise model with additional reduction possible. Noise which triggers the DeepCore detector is evaluated and rejected using the NoiseEngine filtering module. Minimal cleaning removes 94% of noise triggers while retaining 85% of ν_e and 87% of ν_μ events with energies of 10-300 GeV. Stringent cleaning removes 99.9% of noise triggers while retaining only 55% of ν_e and 60\% of ν_μ signal events in the same energy range.
dc.format.extent 56 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Physics
dc.subject.other Astrophysics
dc.title Simulation and identification of non-Poissonian noise triggers in the IceCube neutrino detector
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Dept. of Physics and Astronomy
etdms.degree.discipline Physics
etdms.degree.grantor The University of Alabama
etdms.degree.level master's
etdms.degree.name M.S.


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